Develop model selection frameworks

About this Video

• Video Title: Develop model selection frameworks
• Channel: Gooru Content
• Speakers: None specified in transcript
• Duration: 00:04:38

Introduction

This video outlines a structured methodology for selecting foundation models for generative AI development. It emphasizes the importance of a strategic approach to choosing the right model, considering not only performance metrics but also costs, risks, and ethical implications. The video details a multi-step process to ensure optimal model selection for current and future needs.

Key Takeaways

• Define granular requirements: Go beyond simple performance metrics; consider task type, quality metrics (accuracy, creativity), domain specificity, latency constraints, cost, data sensitivity, and deployment options (cloud, on-premise).
• Scout the landscape: Identify potential models from commercial APIs, managed cloud services, and open-source options. Apply hard constraints early to narrow possibilities to a short list (3-5).
• Rigorous vetting: Test short-listed models using custom test datasets reflecting real-world scenarios. Use automated metrics and human-in-the-loop evaluation for objective and subjective assessments. A proof-of-concept implementation is recommended.
• Comprehensive TCO (Total Cost of Ownership) analysis: Include inference, licensing, infrastructure, fine-tuning, and MLOps costs. Assess data security, privacy, bias, ethical risks, and vendor lock-in.
• Iterative process: Model selection is not a one-time event. Formalize the process, build robust test datasets, invest in prompt engineering, and continuously monitor performance.